class_docstr: Leave One Feature Out Importance
  Given a model and cross-validation scheme, calculates the feature importances.
  
  Parameters
  ----------
  dataset: LOFO Dataset object
  scoring: string or callable
      Same as scoring in sklearn API
  model: model (sklearn API), optional
      Not trained model object
  fit_params : dict, optional
      fit parameters for the model
  cv: int or iterable
      Same as cv in sklearn API
  n_jobs: int, optional
      Number of jobs for parallel computation
  cv_groups: array-like, with shape (n_samples,), optional
      Group labels for the samples used while splitting the dataset into train/test set. 
      Only used in conjunction with a “Group” cv instance (e.g., GroupKFold).
code: def _get_cv_score_parallel(self, feature, result_queue):
      test_score = self._get_cv_score(feature_to_remove=feature)
      result_queue.put((feature, test_score))
      return test_score
  
file_path: lofo-importance/lofo/lofo_importance.py
imports: import numpy
  from sklearn.model_selection import cross_validate
  from tqdm.autonotebook import tqdm
  import warnings
  from lofo.infer_defaults import infer_model
  from lofo.utils import lofo_to_df, parallel_apply
label_desc: Use image node_id 3 for calling the LOFOImportance obj's underlying member method code with example usage: obj._get_cv_score_parallel(feature, result_queue) and returns: test_score